Senior Data Scientist I, Core

Fetch•US - Remote,

1d•$159,945 - $188,171

About The Position

The Senior Data Scientist I, Core will help Fetch establish rigorous performance standards and scalable self-service analytics. The role contributes to the company KPI framework and its underlying model, and leads reusable, company-wide analytical frameworks and automation. The work focuses on trusted metrics: standardizing impact measurement and connecting product and business drivers to outcomes such as MAU, retention, ARPU, revenue, and margin. The role validates forecasts and communicates sensitivity and uncertainty to inform planning and operating decisions. The person in this role owns defined KPI, modeling, or measurement workstreams through delivery and adoption; makes actionable recommendations; and translates company goals into workstream targets. They collaborate with cross-functional partners to build reproducible diagnostics and analytical tools, and document and share reusable methods.

Requirements

5+ years of experience in data science, machine learning, or applied analytics, with demonstrated ownership of complex analytical problems in product-driven environments.
Experience working with multiple stakeholders to define KPIs and their hierarchy.
Ability to structure a complex space into a tackle-able project plan.
Track record of establishing trusted metric definitions and driving adoption across Product, Finance, Commercial, and Engineering.
Experience building and validating models that connect user behavior and business drivers to retention, monetization, or company outcomes.
Ability to show sensitivity estimates, leading indicators, time horizons, and out-of-sample predictive performance.
Experience using models in target setting or scenario planning while distinguishing prediction from causal impact.
Experience with ML modeling, including regression, causal inference methodology, and graph models.
Experience modeling multi-metric relationships.
Strong skills in SQL, Python, statistics, causal inference, and forecasting.
Track record of shipping reusable dashboards, diagnostic apps, or analytical frameworks with evidence of accuracy, adoption, and decision impact.
Experience building robust SQL/Python analytical assets and partnering on productionization.
Track record of documenting and sharing reusable methods.
Understanding of experimentation methodology sufficient to identify patterns, concerns, and frameworks that support product data scientists.

Nice To Haves

Consumer product analytics experience connecting acquisition, activation, engagement, retention, and monetization.
Experience owning KPI frameworks, causal measurement, or business planning.
Experience in centralized or platform data science.
Experience with experimentation platforms.
Experience with governed self-service analytics.

Responsibilities

Independently own a company-wide KPI, modeling, or measurement workstream through delivery and adoption.
Contribute to the core company KPI framework, including its driver tree and sensitivity model.
Contribute analysis to planning and points allocation.
Define certified metrics and resolve conflicting definitions with partners.
Build KPI trees and decompositions connecting product and business drivers to MAU, retention, ARPU, revenue, and margin.
Model KPI drivers and quantify the direction, sensitivity, predictive strength, and time horizon of KPI-to-North-Star relationships.
Distinguish arithmetic identities, predictive associations, and causal effects.
Build scenarios and translate company goals into workstream targets.
Apply established observational causal methods with diagnostics and robustness checks, making identification assumptions explicit.
Build reproducible diagnostics and analytical tools grounded in certified data.
Evaluate the accuracy, adoption, and sustained value of work delivered.
Build reproducible, tested analytical workflows in SQL and Python.
Partner with Data Engineering on gold tables, data marts, and semantic layers.
Partner cross-functionally with teams such as Product and Engineering to drive adoption.
Make actionable recommendations and clearly communicate sensitivity, assumptions, and uncertainty.